Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis
Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such thr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/9/1034 |
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| author | David Cuesta-Frau Pradeepa H. Dakappa Chakrapani Mahabala Arjun R. Gupta |
| author_facet | David Cuesta-Frau Pradeepa H. Dakappa Chakrapani Mahabala Arjun R. Gupta |
| author_sort | David Cuesta-Frau |
| collection | DOAJ |
| description | Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such threshold that are also representative of the subject status. In this paper, we propose to utilize continuous body temperature time series of patients that developed a fever, in order to apply a method capable of diagnosing the specific underlying fever cause only by means of a pattern relative frequency analysis. This analysis was based on a recently proposed measure, Slope Entropy, applied to a variety of records coming from dengue and malaria patients, among other fever diseases. After an input parameter customization, a classification analysis of malaria and dengue records took place, quantified by the Matthews Correlation Coefficient. This classification yielded a high accuracy, with more than 90% of the records correctly labelled in some cases, demonstrating the feasibility of the approach proposed. This approach, after further studies, or combined with more measures such as Sample Entropy, is certainly very promising in becoming an early diagnosis tool based solely on body temperature temporal patterns, which is of great interest in the current Covid-19 pandemic scenario. |
| first_indexed | 2024-03-10T16:18:48Z |
| format | Article |
| id | doaj.art-9addac2d9e734778a78b928e63d96699 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T16:18:48Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-9addac2d9e734778a78b928e63d966992023-11-20T13:49:29ZengMDPI AGEntropy1099-43002020-09-01229103410.3390/e22091034Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential DiagnosisDavid Cuesta-Frau0Pradeepa H. Dakappa1Chakrapani Mahabala2Arjun R. Gupta3Technological Institute of Informatics, Universitat Politècnica de València, Alcoi Campus, 03801 Alcoi, SpainClinical Pharmacology, Nanjappa Hospitals, Shimoga 91903, IndiaDepartment of Medicine, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal 575001, IndiaDepartment of Medicine, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal 575001, IndiaFever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such threshold that are also representative of the subject status. In this paper, we propose to utilize continuous body temperature time series of patients that developed a fever, in order to apply a method capable of diagnosing the specific underlying fever cause only by means of a pattern relative frequency analysis. This analysis was based on a recently proposed measure, Slope Entropy, applied to a variety of records coming from dengue and malaria patients, among other fever diseases. After an input parameter customization, a classification analysis of malaria and dengue records took place, quantified by the Matthews Correlation Coefficient. This classification yielded a high accuracy, with more than 90% of the records correctly labelled in some cases, demonstrating the feasibility of the approach proposed. This approach, after further studies, or combined with more measures such as Sample Entropy, is certainly very promising in becoming an early diagnosis tool based solely on body temperature temporal patterns, which is of great interest in the current Covid-19 pandemic scenario.https://www.mdpi.com/1099-4300/22/9/1034Slope Entropytime series classificationbody temperaturefeverMatthews Correlation Coefficientmalaria |
| spellingShingle | David Cuesta-Frau Pradeepa H. Dakappa Chakrapani Mahabala Arjun R. Gupta Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis Entropy Slope Entropy time series classification body temperature fever Matthews Correlation Coefficient malaria |
| title | Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis |
| title_full | Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis |
| title_fullStr | Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis |
| title_full_unstemmed | Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis |
| title_short | Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis |
| title_sort | fever time series analysis using slope entropy application to early unobtrusive differential diagnosis |
| topic | Slope Entropy time series classification body temperature fever Matthews Correlation Coefficient malaria |
| url | https://www.mdpi.com/1099-4300/22/9/1034 |
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